AI vs. Machine Learning: Untangling the Concepts

Artificial intelligence (AI) and machine learning (ML) are two terms that are often used interchangeably, leading to confusion about their actual meanings and differences. While they are closely related, they are not quite the same thing. Understanding the nuances between them is essential for anyone seeking to navigate the rapidly evolving landscape of modern technology. This article aims to clarify the relationship between AI and machine learning, exploring their definitions, capabilities, applications, and how they work together.

Introduction to Artificial Intelligence

Artificial intelligence (AI) is a broad field of computer science focused on creating machines capable of intelligent behavior. It aims to simulate human cognitive functions such as learning, problem-solving, and decision-making. AI is basically the intelligence - how we make machines intelligent. AI includes designing systems that can perform tasks requiring human intelligence. These tasks include reasoning, learning, problem-solving, perception, and natural language understanding. The goal of AI is to create computer systems that can imitate the human brain. The goal is to create intelligence that is artificial -- hence the name.

AI systems can be rule-based or data-driven and are designed to mimic human cognitive abilities. AI can be autonomous and learn on its own.

Types of AI

AI can be categorized into different types based on its capabilities:

Narrow AI (ANI): Also considered “weak” AI, ANI is specialized for specific tasks. These systems are designed for specific tasks (e.g., Siri, chatbots). We define weak AI by its ability to complete a specific task, like winning a chess game or identifying a particular individual in a series of photos. Natural language processing and computer vision, which let companies automate tasks and underpin chatbots and virtual assistants such as Siri and Alexa, are examples of ANI.
General AI (AGI): AGI is a hypothetical system with human-like intelligence across various tasks. Stronger forms of AI, like AGI and ASI, incorporate human behaviors more prominently, such as the ability to interpret tone and emotion. Strong AI is defined by its ability compared to humans. AGI would perform on par with another human.
Super AI (ASI): ASI is a theoretical form of AI that surpasses human intelligence in all aspects, including creativity, decision-making, and problem-solving. ASI, also known as superintelligence, would surpass a human’s intelligence and ability.

Applications of AI

AI has a myriad of applications across industries and verticals. The healthcare field utilizes AI in many ways to help medical staff understand risks, streamline procedures, and overall improve patient outcomes. Using AI, representatives can equip themselves to best support customers. This can make it an invaluable tool for people in such a fast-paced industry. Smart call routing leans on AI to route incoming calls to the correct AI agent for each expertise. Online chatbots which can interact with customers and solve common disputes are run off of simple AI. For a long time, the financial sector had roles that could only be done by humans with specialized degrees and training. But now, AI can help to fulfill those roles, as well as provide complete accuracy and speed while doing them.

Here are some specific examples of AI applications:

Self-driving cars: AI algorithms analyze surroundings and make driving decisions.
Healthcare: AI is used to diagnose diseases using medical data and in medical image analysis for better medical diagnosis.
Finance: AI systems detect fraud or predict market trends.
Customer Service: Virtual assistants, powered by AI, provide automated support.

Understanding Machine Learning

Machine learning (ML) is a subset of AI that focuses on teaching machines to learn patterns from data and improve their performance over time. Machine learning is an application of AI. It’s the process of using mathematical models of data to help a computer learn without direct instruction. This enables a computer system to continue learning and improving on its own, based on experience.

Machine learning is the implementation of the compute methods that support AI. The way I think of it is: AI is the science and machine learning is the algorithms that make the machines smarter. So the enabler for AI is machine learning.

The machine isn’t given specific instructions on how to perform a task. Instead of being bound to static rules, ML systems are able to identify relationships within the data they process and adjust their approaches based on these insights. A common tool in data science, predictive analytics interprets historical data in order to make predictions about the future. It does that using techniques like data mining, modeling, machine learning, artificial intelligence, and statistics.

Types of Machine Learning

There are three primary types of machine learning:

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Supervised Learning: This is when researchers tell the machine what the correct answer is for a particular input. For example, they show it an image of a car and tell it the correct answer is “car.” It is the most common technique for training neural networks and other machine learning architectures. Classic or “nondeep” machine learning depends on human intervention to allow a computer system to identify patterns, learn, perform specific tasks and provide accurate results. For example, let’s say I showed you a series of images of different types of fast food: “pizza,” “burger” and “taco.” A human expert working on those images would determine the characteristics distinguishing each picture as a specific fast food type. The bread in each food type might be a distinguishing feature.
Unsupervised Learning/Predictive Learning: Humans and animals learn, typically, in an unsupervised manner by watching how the world works and by observing our parents. However, no-one is there to tell us the name and function of every object we perceive so we have to teach ourselves basic concepts such as: the world is three-dimensional, objects don't disappear spontaneously and objects that are not supported fall. While the subset of AI called deep machine learning can leverage labeled data sets to inform its algorithm in supervised learning, it doesn’t necessarily require a labeled data set. It can ingest unstructured data in its raw form (for example, text, images), and it can automatically determine the set of features that distinguish “pizza,” “burger” and “taco” from one another.
Reinforcement Learning: This type of learning concentrates on how an AI 'agent' should behave in order to get the most out of its work. The machine picks an action or a sequence of actions, and gets a reward. This is used when teaching machines to play and win games but needs a large number of trials to learn even simple tasks. A third category of machine learning is reinforcement learning, where a computer learns by interacting with its surroundings and getting feedback (rewards or penalties) for its actions.

Applications of Machine Learning

Like AI, the applications of ML are both numerous and varied. These applications tend to be more specific and prescriptive. Many of the major social media platforms utilize ML to help in their moderation process. This helps to flag and identify posts that violate community standards. This is perhaps the most obvious application of ML. The algorithm behind this program recognizes specific patterns in facial features and assigns them to a name. One of the best tools a business can have is the ability to project possibilities of their future. This can help to prevent poor decisions, support smart ones, and overall inform the business leaders of the best path to take. Imagine knowing what part of your car would break down next. You could preemptively fix or replace it and save yourself a headache. That’s what predictive maintenance is all about. Risk is a part of any business. It can come in the form of equipment breaking, bad deals, price fluctuations, and many other things. Risk modeling is a form of predictive analytics that takes in a wide range of data points collected over time and uses those to identify possible areas of risk. The next best action use of predictive analytics takes in data points around customer behavior (such as buying patterns, consumer behavior, social media presence, etc).

Here are some specific examples of ML applications:

Predictive Maintenance: ML algorithms predict when equipment might break down.
Risk Modeling: ML identifies potential areas of risk in business operations.
Facial Recognition: ML algorithms recognize patterns in facial features and assign them to a name.
Social Media Moderation: ML helps flag and identify posts that violate community standards.
Search Result Optimization: When you make a typo, for instance, while searching in Google, it gives you the message: "Did you mean…"? This is the result of one of Google's machine learning algorithms; a system that detects what searches you make a couple seconds after making a certain search.

Deep Learning and Neural Networks

As our article on deep learning explains, deep learning is a subset of machine learning. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required. It also enables the use of large data sets, earning the title of scalable machine learning. Observing patterns in the data allows a deep-learning model to cluster inputs appropriately. Taking the same example from earlier, we might group pictures of pizzas, burgers and tacos into their respective categories based on the similarities or differences identified in the images. A deep-learning model requires more data points to improve accuracy, whereas a machine-learning model relies on less data given its underlying data structure.

Neural networks, also called artificial neural networks or simulated neural networks, are a subset of machine learning and are the backbone of deep learning algorithms. Neural networks are made up of node layers, an input layer, one or more hidden layers and an output layer. Each node is an artificial neuron that connects to the next, and each has a weight and threshold value. When one node’s output is above the threshold value, that node is activated and sends its data to the network’s next layer. Training data teach neural networks and help improve their accuracy over time. Once the learning algorithms are fined-tuned, they become powerful computer science and AI tools because they allow us to quickly classify and cluster data. Using neural networks, speech and image recognition tasks can happen in minutes instead of the hours they take when done manually. As mentioned in the explanation of neural networks above, but worth noting more explicitly, the “deep” in deep learning refers to the depth of layers in a neural network. A neural network of more than three layers, including the inputs and the output, can be considered a deep-learning algorithm. Most deep neural networks are feed-forward, meaning they only flow in one direction from input to output. However, you can also train your model through backpropagation, meaning moving in the opposite direction, from output to input.

The Interconnection Between AI and Machine Learning

While AI and machine learning are very closely connected, they’re not the same. Machine learning is considered a subset of AI. Machine learning is how a computer system develops its intelligence. An “intelligent” computer uses AI to think like a human and perform tasks on its own. Machine learning models are created by studying patterns in the data. Data scientists optimize the machine learning models based on patterns in the data. The process repeats and is refined until the models’ accuracy is high enough for the tasks that need to be done.

Machine Learning, on the other hand, is an approach for achieving AI. While Machine Learning is the dominant approach for achieving AI, it is not the only approach. Genetic algorithms: These are algorithms that mimic the process of evolution to solve an optimization problem. Finally, it's worth noting that modern AI systems often combine Machine Learning and non-Machine Learning approaches.

The pair continued that AI isn't magic, it's just maths - albeit really hard maths.

How AI and Machine Learning Work Together

When you’re looking into the difference between artificial intelligence and machine learning, it’s helpful to see how they interact through their close connection. This is how AI and machine learning work together:

An AI system is built using machine learning and other techniques.
Machine learning models are created by studying patterns in the data.
Data scientists optimize the machine learning models based on patterns in the data.
The process repeats and is refined until the models’ accuracy is high enough for the tasks that need to be done.

Benefits of AI and Machine Learning

The connection between artificial intelligence and machine learning offers powerful benefits for companies in almost every industry-with new possibilities emerging constantly.

These are just a few of the top benefits that companies have already seen:

More sources of data input: AI and machine learning enable companies to discover valuable insights in a wider range of structured and unstructured data sources.
Better, faster decision-making: Companies use machine learning to improve data integrity and use AI to reduce human error-a combination that leads to better decisions based on better data.
Increased operational efficiency: With AI and machine learning, companies become more efficient through process automation, which reduces costs and frees up time and resources for other priorities.

Capabilities of AI and Machine Learning

Companies in almost every industry are discovering new opportunities through the connection between AI and machine learning.

These are just a few capabilities that have become valuable in helping companies transform their processes and products:

Predictive analytics: This capability helps companies predict trends and behavioral patterns by discovering cause-and-effect relationships in data.
Recommendation engines: With recommendation engines, companies use data analysis to recommend products that someone might be interested in.
Speech recognition and natural language understanding: Speech recognition enables a computer system to identify words in spoken language, and natural language understanding recognizes meaning in written or spoken language.
Image and video processing: These capabilities make it possible to recognize faces, objects, and actions in images and videos, and implement functionalities such as visual search.
Sentiment analysis: A computer system uses sentiment analysis to identify and categorize positive, neutral, and negative attitudes that are expressed in text.

Generative AI vs. Traditional Machine Learning

In April 2021, we called machine learning a “pervasive and powerful form of AI … changing every industry.”But after ChatGPT-3.5 was released in 2022, many organizations shifted focus to a subfield of AI, generative AI, which can be used to create new content. Traditional machine learning is now an established technology in many organizations, and today leading firms are focusing on use cases for generative AI.

Machine learning is used for many purposes, from predicting customer behavior to assessing potential fraud in bank transactions to creating tailored search results on shopping sites. The data used to fuel machine learning - including generative AI tools - can be numbers in a spreadsheet, text, images, audio, or video. The more data a machine learning model is trained on, the more accurate the model will be.

Generative AI is a newer type of machine learning that can create new content - including text, images, or videos - based on large datasets. Large language models - AI programs that can process and generate text - are a prominent type of generative AI.

Best Use Cases for Generative AI

In addition to its main function, which is generating new content, generative AI is taking over tasks that traditional machine learning has historically performed.

These situations include:

When you’re dealing with everyday language or common images: LLMs have been trained on a large amount of text or images and can be used “off the shelf” to classify and detect things.
When you want a more accessible option: Using generative AI models is something many software engineers can do without a large amount of extra training, whereas building machine learning models requires technical expertise.

When Traditional Machine Learning is the Better Option

In some cases, though, machine learning is still the best option.

Those situations could include:

When you have privacy concerns: You must exercise caution when feeding proprietary, sensitive, or confidential information into LLMs, because there is the potential for data leaks.
When you’re using highly specific domain knowledge: LLMs are trained on widely available data and suited to deal with everyday information. But they may not be as accurate for highly technical or niche tasks.
When you already have a machine learning model: Organizations have put a lot of effort into building machine learning programs for specific applications.

When to Use Machine Learning and Generative AI Together

In several situations, machine learning and generative AI can be used together for better outcomes.

These scenarios include the following:

When you want to augment a machine learning model: Generative AI can provide more context to the world, enhancing the accuracy of machine learning models.
When you want to generate data for a machine learning model: In cases where you don’t have enough data to properly train a traditional machine learning model, generative AI can be used to create synthetic data.
When you want to prepare structured data for a machine learning model: Generative AI can clean tabular data in situations like industrial settings that often contain errors, such as missing values, that need to be addressed before the data can be used to train a model.

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